| import gradio as gr |
| import torch |
| from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
|
|
| MODEL_ID = "Qwen/Qwen2.5-Coder-1.5B-Instruct" |
|
|
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
| model = AutoModelForCausalLM.from_pretrained( |
| MODEL_ID, |
| device_map="auto", |
| ) |
|
|
| SYSTEM_PROMPT = """You are an expert code reviewer. Analyze the provided code and give structured feedback in this exact format: |
| |
| ## 🐛 Bugs & Errors |
| List any bugs, logic errors, or runtime issues. If none, say "None found." |
| |
| ## 🔒 Security Issues |
| List any security vulnerabilities (SQL injection, XSS, hardcoded secrets, etc.). If none, say "None found." |
| |
| ## 🎨 Code Style & Readability |
| Comment on naming conventions, formatting, comments, and clarity. |
| |
| ## ⚡ Performance |
| Identify any inefficiencies or bottlenecks. |
| |
| ## ✅ Suggestions |
| Top 3 actionable improvements the developer should make. |
| |
| Be specific, concise, and constructive. Reference line numbers where possible.""" |
|
|
|
|
| def build_prompt(code: str, language: str) -> str: |
| return f"""<s>[INST] <<SYS>> |
| {SYSTEM_PROMPT} |
| <</SYS>> |
| |
| Please review the following {language} code: |
| |
| ```{language.lower()} |
| {code} |
| ``` |
| [/INST]""" |
|
|
|
|
| def review_code(code: str, language: str, max_new_tokens: int = 800): |
| try: |
| if not code.strip(): |
| return "⚠️ Please paste some code to review." |
| |
| prompt = build_prompt(code, language) |
| inputs = tokenizer(prompt, return_tensors="pt") |
| |
| inputs = {k: v.to(model.device) for k, v in inputs.items()} |
| |
| with torch.no_grad(): |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=512, |
| temperature=0.2, |
| do_sample=True, |
| top_p=0.95, |
| repetition_penalty=1.1, |
| eos_token_id=tokenizer.eos_token_id, |
| pad_token_id=tokenizer.eos_token_id, |
| use_cache=True |
| ) |
| |
| |
| response = tokenizer.decode( |
| outputs[0][inputs["input_ids"].shape[1]:], |
| skip_special_tokens=True |
| ) |
| return response.strip() or "⚠️ Model returned an empty response." |
| except Exception as e: |
| return f"❌ Error:\n\n{str(e)}" |
|
|
| |
| with gr.Blocks(title="AI Code Reviewer", theme=gr.themes.Soft()) as demo: |
| gr.Markdown("# 🔍 AI Code Reviewer\nPowered by CodeLlama-7B · Paste your code and get structured feedback instantly.") |
|
|
| with gr.Row(): |
| with gr.Column(scale=2): |
| language = gr.Dropdown( |
| choices=["Python", "JavaScript", "TypeScript", "Java", "C++", "Go", "Rust", "SQL"], |
| value="Python", |
| label="Language" |
| ) |
| code_input = gr.Code( |
| language="python", |
| label="Your Code", |
| lines=20, |
| value="# Paste your code here...", |
| ) |
| submit_btn = gr.Button("Review Code ↗", variant="primary") |
|
|
| with gr.Column(scale=2): |
| output = gr.Markdown(label="Review", value="*Review will appear here...*") |
|
|
| submit_btn.click( |
| fn=review_code, |
| inputs=[code_input, language], |
| outputs=output, |
| show_progress="full" |
| ) |
|
|
| gr.Examples( |
| examples=[ |
| ["def divide(a, b):\n return a / b\n\nresult = divide(10, 0)\nprint(result)", "Python"], |
| ["SELECT * FROM users WHERE name = '" + "' + username + '", "SQL"], |
| ], |
| inputs=[code_input, language], |
| label="Try an example" |
| ) |
|
|
| demo.queue(max_size=10).launch() |
